Person image generation is an important sub-area of image generation. Many methods have been proposed for pose-guided person image generation.
PG2 [1]} proposes to use a two-stage network "from coarse to fine" to generate person under the guidance of the source image and the keypoint-based target pose.
Def-GAN [2]} introduced a U-net with a deformable skip connection to alleviate the problem of deformation.
Li \(et \ al.\)  [3]} propose to generate a dense 3D appearance flow, which can guide the transfer of pixels between poses.
PATN [4]} introduces the attention mechanism into the generator to solve the problem of deformation in a progressive generation method.
Ren \(et \ al.\)  [5]} regards the target image as the deformation version of the source image, and uses a global flow field estimator to calculate correspondence between sources and targets and a local attention sampling module as a content-aware sampling method to generate the image.
There are also some methods that try to combine pose-guided person image generation with clothing-guided person image generation.
Based on VAE, VUnet [6]} proposed the conditional U-net to manipulate pose and appearance.
Zhou \(et \ al.\)  [7]}uses a text-guided approach to transform the appearance of person,including pose and attribute.
Men \(et \ al.\)  [8]} proposed to decouple person attributes by the existing human parsing method, which not only realizes pose transfer but also realizes the control of person attributes.
However, all methods above usually either only focus on pose-guided person image generation or simply combine pose-guided person image generation and clothing-guided person image generation. Our method solves the problem of deep feature misalignment by establishing the dense correspondence between the target pose and the source image in the deep feature space. A joint learning method that effectively combines pose-guided person image generation task and clothing-guided person image generation to generate high-quality person images.
